For mobile communications systems multipath downlink transmission has received a lot of attention and many efforts have been done to enable an efficient handling of power in the base station, also with the aim of enabling a good receiving possibility in the user station. Since multiple antennas are used, signals on the respective antennas have to be transmitted with an appropriate power, which normally is handled by applying so called downlink weight vectors.
Different approaches have been implemented which all are based on utilizing different types of data for determining the downlink weight vector.
According to one method received uplink power spectra are used to estimate direction of arrival (DOA) for the signals. Such a method can be applied also when one can not rely on channel reciprocity and transmission instead is based on second order statistics. If, on the other hand, channel reciprocity is applicable, it is possible to calculate eigenvectors for a channel correlation matrix and then to use these eigenvectors for downlink transmission. The weight vector is based on a channel estimation for received signals and contains phase and amplitude information. Through the use of an eigenvector based approach, the antenna configuration does not have to be known in order to implement the channel estimation algorithm, which is an advantage compared to methods based on estimating direction of arrival. There are however problems associated with all known methods. Two of these problems are that power utilization in a base station can not be controlled to a satisfactory extent and that power is not optimzed.
Methods based on eigenvalue decomposition of the estimated channel correlation matrix information use eigenvalues and eigenvectors. The eigenvalues are measures of the channel quality and the eigenvectors are the weightvectors to be used. For some antenna arrangements an eigenvector can be interpreted as a conventional beam-forming vector. FIG. 1 for example shows a radio base station RBS 200 with (here) four antenna elements having the same polarization connected thereto over antenna ports, on the antenna part, and feeder ports, which are the ports of the RBS (Radio Base Station).
The antenna part may e.g. comprise a uniform linear array (ULA) with four antenna elements, with an element spacing dr in the base station. It here communicates with a single antenna in a user equipment UE 90. The antenna elements are located so close to each other, typically half a wavelength, that the radio channel between the antenna in UE 90 and the ULA in RBS 200 in many cases are highly correlated. This means that they are almost identical for all base station antenna elements, except for a direction of arrival dependant phase shift corresponding to the difference in path length in communication with the UE. The signal received in RBS 200 can, for a radio channel with a small angular spread, i.e., for highly correlated radio channels between the UE antenna and base station antennas be expressed as:y=(a(φ)c)x+n=hx+n, wherein a(φ) is the array response vector, and φ is a spatial angle corresponding to the direction of arrival of the signal.a(φ)=[eJ(−(N−1)/2kd sin(φ))) ej(−(N−3)/2kd sin(φ))) . . . ej((N−1)/2kd sin(φ)))]T 
The total radio channel h, which is estimated from the received signal vector, is composed by the array response vector and a complex channel amplification c which is assumed to be identical for all antenna elements, the transmitted signal is denoted x and interference including thermal noise is denoted n. The covariance matrix for the (total) radio channel is found to be:Rh,h=E{hhH}=a(φ)ccHa(φ)H+Rn,n=ccHa(φ)a(φ)H+Rnn 
If an eigenvalue decomposition of Rh,h is performed, eigenvalues D and eigenvectors V are obtained such that:Rh,hV=VD
As the channel rank is 1 in this case with only one antenna at the UE, there is only one eigenvalue>0. The eigenvector corresponding to that eigenvalue is a replica of the array response a(φ) except for a complex scaling factor. Thus, if this vector is applied as a transmit weight vector, a beam pointing in the direction of the UE will result.
Further, in this example all elements in the weight vector will have the same magnitude but this is not the general case. When channel correlation is low, which for example occurs if the antenna is dual polarized or if the element separation is large there will be a magnitude variation over the elements. This is exemplified in FIG. 2 which shows a state of the art arrangement with an antenna part 10′0 with dual polarized antenna means, a processing unit 210 which performs an eigenvalue decomposition of channel estimates for finding an eigenvector to be applied for feeding the antenna elements.
If separate power amplifiers separately feed individual branches or antenna element formations, which is a very common implementation, the magnitude variation in the weight vector results in that, since the largest magnitude will limit the output power in order not to overload the power amplifier, the available power resources will not be utilized in an optimal way (unless somehow used by other simultaneous users) and resource usage will be limited
Conventionally an eigenvalue decomposition is performed over all elements in the antenna array to find eigenvalues and eigenvectors. The finding of eigenvalues and eigenvectors does not require any information about the architecture of the antenna array, which traditionally has been seen as an advantage. Typically the amplitude varies over the elements in the weight vector (eigenvector) which results in a reduction of the available maximal output power to the concerned UE for common radio architectures.